NOTE:

THIS IS FROM "DISCRETE MATH" COURSE FOR COMPUTER SCIENCE

I RECOMMEND YOU TO DO THIS ASSIGNMENT ON VISUAL STUDIO

SINCE I HAVE NEVER TAKING C++, I MAY HAVE SOME DIFFICULTY FOR THIS ASSIGNMENT. THEREFORE, I HOPE YOU CAN DO THIS ASSIGNMENT THOROUGHLY

BELOW IS TWO OF THE SAMPLES TO TEST THE PROGRAM ON, WHICH IS "email1.txt" AND ""spam1.txt" FOR each folder testFiles and smallTestFiles that my professor included for this assignment.

I JUST POST TWO FOR EACH FOLDER(TESTFILES and SMALLTESTFILES) SINCE THERE ARE STILL MORE in each folder , SO I POST THEM HERE IN ORDER FOR YOU TO HAVE AN IDEA TO WRITE THE CODE ON THIS.

*From smallTestFiles folder* (Total 4 files)

email1.txt

hi, thank you so much! you're the best!

spam1.txt

you've been selected as a winner! click now to get the best anti-virus scanner!

*From "testFiles" folder* (Total 10 files)

email1.txt

dear all,

on behalf of thrill company, i am glad to invite you for a luncheon party with all the senior employees, team members, and other staff members associated with the company. since according to associations policies, we have five working days, therefore we have planned to set a lunch party for saturday, 13th january 2012.

please mark your presence on this party. together, we would get an opportunity to interact with our boss, expand our contacts, learn more about our field and of course eat some mouth watering dishes. this luncheon would be held in a new york cafe situated at park lane. kindly be present by 12:00 noon so that your taste buds do not miss any of the tempting dishes being served!

i request you to confirm your presence latest by wednesday, 9th january 2012 so that we make appropriate bookings.

looking forward to see you on this thrills luncheon party!

sincerely, jacob thomas hr head thrill company

spam1.txt

it training tuition scholarships for college faculty, students and staff

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for either program, registration is valid through june 30, 2001 and there are no tuition costs for classes. the registrant receives free unlimited access to the courses, a vast online library, chat areas, certification skill tests and evaluations. this is an exceptional value and a great way for anyone to upgrade it skills and learn new skills.

to sign up, visit www.cyberlearning.org and click on "pc scholarships(300+ courses)" or click on "it scholarships (650+ courses)." then, complete the "teachers and others in education" application. manycolleges, schools and other educational organizations reimburse the training registration fee. over 5,000 educators, faculty and students have already registered.

to bridge the digital divide, nef also provides "no excuse" it training scholarships to disadvantaged school and college students and teachers throughout the nation.

please forward this information to all interested colleagues and others in education, colleges, universities and schools.

to unsubscribe, please reply with "unsubscibe" in the subject line.

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nef is well on its way to training 100,000 it professionals and a million disadvantaged students nationally through its "no excuse" it training program. nef has earned many distinctions including "the ivy league of it training," "1995 fairfax human rights award," and " a leader in bridging the digital divide."

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"i have found the cyberlearning online courses to be extremely easy and useful. i liked pre-course self-assessment and it books online and available 24/7. the course screens were interactive and made me feel as if i was in the application itself. the site looks and feels very professional. the list of courses is huge. it includes something for almost everyone. i find this to be a very worthy cause." --- ken horowitz, it training coordinator

PROMPT:

Lab 2:Spam Filter In this lab, you will implement part of a naive Bayes' spam classifier. To illustrate how this filter works, consider the following email: Hey! This is the best link I found. I thought you would want to see it! www.somelink. com/example Best, Sus We want to classify this email as either spam or not spam. Typically, the filter will consider the emails. For our filter, we will entire email and look for multiple words that are common in spam consider a single word For this example, we will classify the email based on the word "best". Assume the probability that any particular email is spam is 0.25, and the probability that any particular email is not spam is 0.75 To classify the mystery email (above), we want to compute the probability that this email is spam given that it contains the word "best". Then we want to compute the probability that this email is NOT spam given that it contains the word "best". We then classify based on which probability is higher Let's define a couple of variables 1. C: email contains the word "best" 2T: email does NOT contain the word "best" 3. S: email is spam 4. S: email is NOT spam Hence, we want to compute P(SC) and P(SC) Computing the Probability of "best" First, we need to figure out how common "best" is in spam emails and how common "best" is in emails that are not spam. To do this, we have to use sample emails. This is called training data. For this example, we'll use the following emails. These are the sample spam emails 1. vou've been selected as a winner! click now to get the best anti-virus canner! 2. you're a winner! reply immediately to claim your access to the best weight loss system ever. Lab 2:Spam Filter In this lab, you will implement part of a naive Bayes' spam classifier. To illustrate how this filter works, consider the following email: Hey! This is the best link I found. I thought you would want to see it! www.somelink. com/example Best, Sus We want to classify this email as either spam or not spam. Typically, the filter will consider the emails. For our filter, we will entire email and look for multiple words that are common in spam consider a single word For this example, we will classify the email based on the word "best". Assume the probability that any particular email is spam is 0.25, and the probability that any particular email is not spam is 0.75 To classify the mystery email (above), we want to compute the probability that this email is spam given that it contains the word "best". Then we want to compute the probability that this email is NOT spam given that it contains the word "best". We then classify based on which probability is higher Let's define a couple of variables 1. C: email contains the word "best" 2T: email does NOT contain the word "best" 3. S: email is spam 4. S: email is NOT spam Hence, we want to compute P(SC) and P(SC) Computing the Probability of "best" First, we need to figure out how common "best" is in spam emails and how common "best" is in emails that are not spam. To do this, we have to use sample emails. This is called training data. For this example, we'll use the following emails. These are the sample spam emails 1. vou've been selected as a winner! click now to get the best anti-virus canner! 2. you're a winner! reply immediately to claim your access to the best weight loss system ever